{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53031f81-cebd-4a18-a5f9-4a462a0a56b1",
   "metadata": {},
   "source": [
    "# 一、空气污染数据分析思路"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7294bfd1-c2f2-4563-8e02-40d89351ea8d",
   "metadata": {},
   "source": [
    "- 明确需求\n",
    "    - 为什么要做数据分析、分析什么、想要达到什么样的效果\n",
    "- 获取数据\n",
    "    - 数据哪儿来，获取什么范围的数据，用什么方式去获取数据\n",
    "- 数据预处理\n",
    "    - 数据规约、数据清洗、数据加工\n",
    "- 数据分析\n",
    "    - 描述性统计分析、相关分析、回归分析、时序分析、分类分析\n",
    "- 数据可视化\n",
    "    - 可视化用图表的方式进行呈现，使结果清晰直观，便于理解\n",
    "- 数据应用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bb806241-0f02-4bed-b6eb-061e55564585",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import os\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e662787f-1802-4576-91c4-18ea6cb4dba2",
   "metadata": {},
   "source": [
    "## （1）明确需求"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f1e2d8-3993-404c-a885-9823110a7815",
   "metadata": {},
   "source": [
    "## （2）获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2eac6da8-a682-49d0-a9ba-9b2494df856e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>time</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>complexindex</th>\n",
       "      <th>rank</th>\n",
       "      <th>primary_pollutant</th>\n",
       "      <th>temp</th>\n",
       "      <th>humi</th>\n",
       "      <th>windlevel</th>\n",
       "      <th>winddirection</th>\n",
       "      <th>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>0.7</td>\n",
       "      <td>37</td>\n",
       "      <td>2.93244</td>\n",
       "      <td>173.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>0.000</td>\n",
       "      <td>90.042</td>\n",
       "      <td>1.958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41</td>\n",
       "      <td>3.57411</td>\n",
       "      <td>220.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>0.333</td>\n",
       "      <td>92.238</td>\n",
       "      <td>1.191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>98</td>\n",
       "      <td>73</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4.67262</td>\n",
       "      <td>207.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>2.739</td>\n",
       "      <td>89.522</td>\n",
       "      <td>0.304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>2.87827</td>\n",
       "      <td>150.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>3.542</td>\n",
       "      <td>95.917</td>\n",
       "      <td>2.042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>188</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>1.2</td>\n",
       "      <td>42</td>\n",
       "      <td>5.54940</td>\n",
       "      <td>323.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>4.182</td>\n",
       "      <td>88.500</td>\n",
       "      <td>1.818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  province city        time  aqi  pm2_5  pm10  so2  no2   co  o3  \\\n",
       "0       湖南   长沙  2019-01-01   64     46    37    5   24  0.7  37   \n",
       "1       湖南   长沙  2019-01-02  100     75     0    6   33  1.0  41   \n",
       "2       湖南   长沙  2019-01-03   98     73    72    5   46  1.0  12   \n",
       "3       湖南   长沙  2019-01-04   63     45    25    4   31  1.0  23   \n",
       "4       湖南   长沙  2019-01-05  188    141     0    5   35  1.2  42   \n",
       "\n",
       "   complexindex   rank primary_pollutant   temp    humi  windlevel  \\\n",
       "0       2.93244  173.0        细颗粒物:PM2.5  0.000  90.042      1.958   \n",
       "1       3.57411  220.0        细颗粒物:PM2.5  0.333  92.238      1.191   \n",
       "2       4.67262  207.0        细颗粒物:PM2.5  2.739  89.522      0.304   \n",
       "3       2.87827  150.0        细颗粒物:PM2.5  3.542  95.917      2.042   \n",
       "4       5.54940  323.0        细颗粒物:PM2.5  4.182  88.500      1.818   \n",
       "\n",
       "   winddirection weather  \n",
       "0            NaN     NaN  \n",
       "1            NaN     NaN  \n",
       "2            NaN     NaN  \n",
       "3            NaN     NaN  \n",
       "4            NaN     NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入数据\n",
    "df = pd.read_csv('../../data_five_year/湖南.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c4e086d-8efb-45ed-b407-dc4777177661",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1825 entries, 0 to 1824\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   province           1825 non-null   object \n",
      " 1   city               1825 non-null   object \n",
      " 2   time               1825 non-null   object \n",
      " 3   aqi                1825 non-null   int64  \n",
      " 4   pm2_5              1825 non-null   int64  \n",
      " 5   pm10               1825 non-null   int64  \n",
      " 6   so2                1825 non-null   int64  \n",
      " 7   no2                1825 non-null   int64  \n",
      " 8   co                 1825 non-null   float64\n",
      " 9   o3                 1825 non-null   int64  \n",
      " 10  complexindex       1825 non-null   float64\n",
      " 11  rank               1796 non-null   float64\n",
      " 12  primary_pollutant  1704 non-null   object \n",
      " 13  temp               1823 non-null   float64\n",
      " 14  humi               1823 non-null   float64\n",
      " 15  windlevel          1823 non-null   float64\n",
      " 16  winddirection      0 non-null      float64\n",
      " 17  weather            1029 non-null   object \n",
      "dtypes: float64(7), int64(6), object(5)\n",
      "memory usage: 256.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eeb4301d-23ac-41fe-9609-bfdf4ce1ba13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 56604 entries, 0 to 56603\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   province           56604 non-null  object \n",
      " 1   city               56604 non-null  object \n",
      " 2   time               56604 non-null  object \n",
      " 3   aqi                56604 non-null  int64  \n",
      " 4   pm2_5              56604 non-null  int64  \n",
      " 5   pm10               56604 non-null  int64  \n",
      " 6   so2                56604 non-null  int64  \n",
      " 7   no2                56604 non-null  int64  \n",
      " 8   co                 56604 non-null  float64\n",
      " 9   o3                 56604 non-null  int64  \n",
      " 10  complexindex       56604 non-null  float64\n",
      " 11  rank               55624 non-null  float64\n",
      " 12  primary_pollutant  53202 non-null  object \n",
      " 13  temp               56572 non-null  float64\n",
      " 14  humi               56572 non-null  float64\n",
      " 15  windlevel          56572 non-null  float64\n",
      " 16  winddirection      0 non-null      float64\n",
      " 17  weather            31928 non-null  object \n",
      "dtypes: float64(7), int64(6), object(5)\n",
      "memory usage: 7.8+ MB\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>time</th>\n",
       "      <th>aqi</th>\n",
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       "      <th>humi</th>\n",
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       "      <th>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>68</td>\n",
       "      <td>2.69167</td>\n",
       "      <td>66.0</td>\n",
       "      <td>—</td>\n",
       "      <td>3.625</td>\n",
       "      <td>60.292</td>\n",
       "      <td>0.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>87</td>\n",
       "      <td>64</td>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>0.9</td>\n",
       "      <td>68</td>\n",
       "      <td>4.84881</td>\n",
       "      <td>188.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>4.458</td>\n",
       "      <td>61.125</td>\n",
       "      <td>0.250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>58</td>\n",
       "      <td>0.7</td>\n",
       "      <td>55</td>\n",
       "      <td>4.21875</td>\n",
       "      <td>135.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>6.958</td>\n",
       "      <td>66.000</td>\n",
       "      <td>0.292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>75</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>0.6</td>\n",
       "      <td>47</td>\n",
       "      <td>2.87232</td>\n",
       "      <td>181.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>9.048</td>\n",
       "      <td>91.286</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>0.7</td>\n",
       "      <td>42</td>\n",
       "      <td>3.19821</td>\n",
       "      <td>175.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>7.708</td>\n",
       "      <td>96.667</td>\n",
       "      <td>0.083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "  province city        time  aqi  pm2_5  pm10  so2  no2   co  o3  \\\n",
       "0       上海   上海  2019-01-01   40     28    35    7   29  0.5  68   \n",
       "1       上海   上海  2019-01-02   87     64    72   13   45  0.9  68   \n",
       "2       上海   上海  2019-01-03   73     47    53    9   58  0.7  55   \n",
       "3       上海   上海  2019-01-04   75     18    22    6   60  0.6  47   \n",
       "4       上海   上海  2019-01-05   64     32    33    6   51  0.7  42   \n",
       "\n",
       "   complexindex   rank primary_pollutant   temp    humi  windlevel  \\\n",
       "0       2.69167   66.0                 —  3.625  60.292      0.500   \n",
       "1       4.84881  188.0        细颗粒物:PM2.5  4.458  61.125      0.250   \n",
       "2       4.21875  135.0              二氧化氮  6.958  66.000      0.292   \n",
       "3       2.87232  181.0              二氧化氮  9.048  91.286      0.000   \n",
       "4       3.19821  175.0              二氧化氮  7.708  96.667      0.083   \n",
       "\n",
       "   winddirection weather  \n",
       "0            NaN     NaN  \n",
       "1            NaN     NaN  \n",
       "2            NaN     NaN  \n",
       "3            NaN     NaN  \n",
       "4            NaN     NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入所有省份的数据合成一个df_all\n",
    "# 获取文件路径\n",
    "file_path = \"../../data_five_year/\"\n",
    "file_names = os.listdir(file_path)\n",
    "# 导入所有省份把他们合并\n",
    "df_list = []\n",
    "for file_name in file_names:\n",
    "    if file_name.endswith('.csv'):  # 确保格式正确\n",
    "        df = pd.read_csv(os.path.join(file_path, file_name))\n",
    "        df_list.append(df)\n",
    "# 合并所有的df到df_all\n",
    "df_all = pd.concat(df_list, ignore_index=True)  # 重置合并后的索引\n",
    "display(df_all.head(), df_all.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a86f83-3075-454f-bc59-b0743cac8dde",
   "metadata": {},
   "source": [
    "## （3）数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594d9eff-5552-49aa-b924-f75e9fde9c9a",
   "metadata": {},
   "source": [
    "### ①time时间数据是日期格式，并把日期设置为索引"
   ]
  },
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       "      <td>150.0</td>\n",
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       "      <td>323.0</td>\n",
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       "      <td>4.182</td>\n",
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       "      <td>晴</td>\n",
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       "      <td>7.67530</td>\n",
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       "      <td>10.200</td>\n",
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       "<p>1825 rows × 18 columns</p>\n",
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      "text/plain": [
       "     province city       time  aqi  pm2_5  pm10  so2  no2   co  o3  \\\n",
       "0          湖南   长沙 2019-01-01   64     46    37    5   24  0.7  37   \n",
       "1          湖南   长沙 2019-01-02  100     75     0    6   33  1.0  41   \n",
       "2          湖南   长沙 2019-01-03   98     73    72    5   46  1.0  12   \n",
       "3          湖南   长沙 2019-01-04   63     45    25    4   31  1.0  23   \n",
       "4          湖南   长沙 2019-01-05  188    141     0    5   35  1.2  42   \n",
       "...       ...  ...        ...  ...    ...   ...  ...  ...  ...  ..   \n",
       "1820       湖南   长沙 2023-12-27  130     99   141    7   61  1.1   0   \n",
       "1821       湖南   长沙 2023-12-28  231    181   222    6   41  1.1  88   \n",
       "1822       湖南   长沙 2023-12-29  251    201   250    6   34  1.2  62   \n",
       "1823       湖南   长沙 2023-12-30  231    181   215    6   45  1.4  72   \n",
       "1824       湖南   长沙 2023-12-31  168    127   158    5   34  1.1  93   \n",
       "\n",
       "      complexindex   rank primary_pollutant    temp    humi  windlevel  \\\n",
       "0          2.93244  173.0        细颗粒物:PM2.5   0.000  90.042      1.958   \n",
       "1          3.57411  220.0        细颗粒物:PM2.5   0.333  92.238      1.191   \n",
       "2          4.67262  207.0        细颗粒物:PM2.5   2.739  89.522      0.304   \n",
       "3          2.87827  150.0        细颗粒物:PM2.5   3.542  95.917      2.042   \n",
       "4          5.54940  323.0        细颗粒物:PM2.5   4.182  88.500      1.818   \n",
       "...            ...    ...               ...     ...     ...        ...   \n",
       "1820       6.75952    NaN        细颗粒物:PM2.5   9.933  60.583      2.000   \n",
       "1821      10.29286  268.0        细颗粒物:PM2.5   8.950  74.000      2.208   \n",
       "1822      10.95179  324.0        细颗粒物:PM2.5   8.838  76.042      2.333   \n",
       "1823      10.26786    NaN        细颗粒物:PM2.5  10.521  84.792      1.625   \n",
       "1824       7.67530  331.0        细颗粒物:PM2.5  10.200  80.667      2.375   \n",
       "\n",
       "      winddirection weather  \n",
       "0               NaN     NaN  \n",
       "1               NaN     NaN  \n",
       "2               NaN     NaN  \n",
       "3               NaN     NaN  \n",
       "4               NaN     NaN  \n",
       "...             ...     ...  \n",
       "1820            NaN       晴  \n",
       "1821            NaN       晴  \n",
       "1822            NaN       晴  \n",
       "1823            NaN       晴  \n",
       "1824            NaN       晴  \n",
       "\n",
       "[1825 rows x 18 columns]"
      ]
     },
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   "source": [
    "df['time'] = pd.to_datetime(df['time'])\n",
    "df"
   ]
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       "      <td>130</td>\n",
       "      <td>99</td>\n",
       "      <td>141</td>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.75952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>9.933</td>\n",
       "      <td>60.583</td>\n",
       "      <td>2.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>231</td>\n",
       "      <td>181</td>\n",
       "      <td>222</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>1.1</td>\n",
       "      <td>88</td>\n",
       "      <td>10.29286</td>\n",
       "      <td>268.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>8.950</td>\n",
       "      <td>74.000</td>\n",
       "      <td>2.208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>251</td>\n",
       "      <td>201</td>\n",
       "      <td>250</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>1.2</td>\n",
       "      <td>62</td>\n",
       "      <td>10.95179</td>\n",
       "      <td>324.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>8.838</td>\n",
       "      <td>76.042</td>\n",
       "      <td>2.333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-30</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>231</td>\n",
       "      <td>181</td>\n",
       "      <td>215</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>1.4</td>\n",
       "      <td>72</td>\n",
       "      <td>10.26786</td>\n",
       "      <td>NaN</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>10.521</td>\n",
       "      <td>84.792</td>\n",
       "      <td>1.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>168</td>\n",
       "      <td>127</td>\n",
       "      <td>158</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93</td>\n",
       "      <td>7.67530</td>\n",
       "      <td>331.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>10.200</td>\n",
       "      <td>80.667</td>\n",
       "      <td>2.375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>晴</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1825 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           province city  aqi  pm2_5  pm10  so2  no2   co  o3  complexindex  \\\n",
       "time                                                                          \n",
       "2019-01-01       湖南   长沙   64     46    37    5   24  0.7  37       2.93244   \n",
       "2019-01-02       湖南   长沙  100     75     0    6   33  1.0  41       3.57411   \n",
       "2019-01-03       湖南   长沙   98     73    72    5   46  1.0  12       4.67262   \n",
       "2019-01-04       湖南   长沙   63     45    25    4   31  1.0  23       2.87827   \n",
       "2019-01-05       湖南   长沙  188    141     0    5   35  1.2  42       5.54940   \n",
       "...             ...  ...  ...    ...   ...  ...  ...  ...  ..           ...   \n",
       "2023-12-27       湖南   长沙  130     99   141    7   61  1.1   0       6.75952   \n",
       "2023-12-28       湖南   长沙  231    181   222    6   41  1.1  88      10.29286   \n",
       "2023-12-29       湖南   长沙  251    201   250    6   34  1.2  62      10.95179   \n",
       "2023-12-30       湖南   长沙  231    181   215    6   45  1.4  72      10.26786   \n",
       "2023-12-31       湖南   长沙  168    127   158    5   34  1.1  93       7.67530   \n",
       "\n",
       "             rank primary_pollutant    temp    humi  windlevel  winddirection  \\\n",
       "time                                                                            \n",
       "2019-01-01  173.0        细颗粒物:PM2.5   0.000  90.042      1.958            NaN   \n",
       "2019-01-02  220.0        细颗粒物:PM2.5   0.333  92.238      1.191            NaN   \n",
       "2019-01-03  207.0        细颗粒物:PM2.5   2.739  89.522      0.304            NaN   \n",
       "2019-01-04  150.0        细颗粒物:PM2.5   3.542  95.917      2.042            NaN   \n",
       "2019-01-05  323.0        细颗粒物:PM2.5   4.182  88.500      1.818            NaN   \n",
       "...           ...               ...     ...     ...        ...            ...   \n",
       "2023-12-27    NaN        细颗粒物:PM2.5   9.933  60.583      2.000            NaN   \n",
       "2023-12-28  268.0        细颗粒物:PM2.5   8.950  74.000      2.208            NaN   \n",
       "2023-12-29  324.0        细颗粒物:PM2.5   8.838  76.042      2.333            NaN   \n",
       "2023-12-30    NaN        细颗粒物:PM2.5  10.521  84.792      1.625            NaN   \n",
       "2023-12-31  331.0        细颗粒物:PM2.5  10.200  80.667      2.375            NaN   \n",
       "\n",
       "           weather  \n",
       "time                \n",
       "2019-01-01     NaN  \n",
       "2019-01-02     NaN  \n",
       "2019-01-03     NaN  \n",
       "2019-01-04     NaN  \n",
       "2019-01-05     NaN  \n",
       "...            ...  \n",
       "2023-12-27       晴  \n",
       "2023-12-28       晴  \n",
       "2023-12-29       晴  \n",
       "2023-12-30       晴  \n",
       "2023-12-31       晴  \n",
       "\n",
       "[1825 rows x 17 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('time', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f00dcf9-2f33-4570-acf5-b108f67b2a4e",
   "metadata": {},
   "source": [
    "## （4）数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58439d79-6f41-46a9-b004-8d03a017d856",
   "metadata": {},
   "source": [
    "### ① 按月份分组计算平均AQI（以湖南为例）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08f1de59-6b49-4f9c-9d4d-1be62b589de6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2019-01-31    130.419355\n",
       "2019-02-28     73.000000\n",
       "2019-03-31     68.322581\n",
       "2019-04-30     62.533333\n",
       "2019-05-31     73.548387\n",
       "2019-06-30     64.966667\n",
       "2019-07-31     65.225806\n",
       "2019-08-31     88.870968\n",
       "2019-09-30    108.166667\n",
       "2019-10-31     76.032258\n",
       "2019-11-30     86.266667\n",
       "2019-12-31    104.290323\n",
       "2020-01-31     77.419355\n",
       "2020-02-29     59.965517\n",
       "2020-03-31     59.741935\n",
       "2020-04-30     83.300000\n",
       "2020-05-31     75.548387\n",
       "2020-06-30     47.200000\n",
       "2020-07-31     56.838710\n",
       "2020-08-31     66.870968\n",
       "2020-09-30     55.700000\n",
       "2020-10-31     72.870968\n",
       "2020-11-30     69.266667\n",
       "2020-12-31    123.000000\n",
       "2021-01-31    103.645161\n",
       "2021-02-28     80.035714\n",
       "2021-03-31     56.774194\n",
       "2021-04-30     58.966667\n",
       "2021-05-31     56.516129\n",
       "2021-06-30     68.033333\n",
       "2021-07-31     60.838710\n",
       "2021-08-31     61.258065\n",
       "2021-09-30     87.233333\n",
       "2021-10-31     77.258065\n",
       "2021-11-30     82.933333\n",
       "2021-12-31    104.129032\n",
       "2022-01-31    104.322581\n",
       "2022-02-28     65.464286\n",
       "2022-03-31     57.838710\n",
       "2022-04-30     72.733333\n",
       "2022-05-31     66.677419\n",
       "2022-06-30     54.933333\n",
       "2022-07-31     54.580645\n",
       "2022-08-31     61.677419\n",
       "2022-09-30    103.200000\n",
       "2022-10-31     72.935484\n",
       "2022-11-30     60.666667\n",
       "2022-12-31     81.290323\n",
       "2023-01-31    113.967742\n",
       "2023-02-28     82.678571\n",
       "2023-03-31     77.322581\n",
       "2023-04-30     63.066667\n",
       "2023-05-31     61.645161\n",
       "2023-06-30     63.400000\n",
       "2023-07-31     52.774194\n",
       "2023-08-31     76.774194\n",
       "2023-09-30     51.966667\n",
       "2023-10-31     65.333333\n",
       "2023-11-30     65.500000\n",
       "2023-12-31     83.258065\n",
       "Freq: M, Name: aqi, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按月份分组计算平均AQI\n",
    "monthly_aqi = df.groupby(by=pd.Grouper(freq='M')).mean(numeric_only=True)['aqi']\n",
    "monthly_aqi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ef03c1b-aff3-4599-b306-4895fd318d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['aqi'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ae68a135-a3c2-49f7-b5ec-14464718e766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出月平均空气质量指数趋势折线图\n",
    "plt.figure(figsize=(20, 8))\n",
    "monthly_aqi.plot(marker='o')\n",
    "plt.title(\"月平均空气质量指数趋势\", fontsize=18)\n",
    "plt.xlabel(\"月份\", fontsize=18)\n",
    "plt.ylabel(\"平均AQI\", fontsize=18)\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35de0f0-1d1f-4283-bae8-8716937c9488",
   "metadata": {},
   "source": [
    "如上图所示，空气质量指数(AQI)随时间变化趋势。从图中可以观察到：\n",
    "- 冬季空气质量较差，可能是冬季的取暖和过年的烟火增加了空气污染\n",
    "- 夏季空气质量较好，夏季的天气条件有利于污染物扩散\n",
    "- 春秋两季空气质量中等"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfe1179-8630-4f3e-a4c0-0e7d2366ba85",
   "metadata": {},
   "source": [
    "### ② 不同省份和城市的平均AQI热力图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49132926-cd00-480f-9347-cb27f7792419",
   "metadata": {},
   "source": [
    "**地理位置分析：**\n",
    "- 数据集合：按照省份和城市计算平均AQI\n",
    "- 可视化：绘制柱状图或热力图展示不同省份或城市的空气质量差异\n",
    "- 分析总结：根据可视化结果总结分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "57a209af-d241-4e68-9f92-1deb88d911a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "province  city\n",
       "上海        上海      67.418949\n",
       "云南        昆明      51.334064\n",
       "内蒙古       呼和浩特    75.790252\n",
       "北京        北京      78.276013\n",
       "吉林        长春      66.472070\n",
       "四川        成都      76.407996\n",
       "天津        天津      89.334611\n",
       "宁夏        银川      77.437808\n",
       "安徽        合肥      74.355969\n",
       "山东        济南      94.667579\n",
       "山西        太原      94.813253\n",
       "广东        广州      65.273823\n",
       "广西        南宁      51.984666\n",
       "新疆        乌鲁木齐    85.399781\n",
       "江苏        南京      75.762322\n",
       "江西        南昌      67.444688\n",
       "河北        石家庄     99.263417\n",
       "河南        郑州      97.815991\n",
       "浙江        杭州      71.651150\n",
       "海南        海口      43.872946\n",
       "湖北        武汉      78.246440\n",
       "湖南        长沙      74.406575\n",
       "甘肃        兰州      80.018072\n",
       "福建        福州      51.550931\n",
       "西藏        拉萨      52.811062\n",
       "贵州        贵阳      46.776013\n",
       "辽宁        沈阳      73.671413\n",
       "重庆        重庆      66.189485\n",
       "陕西        西安      95.228368\n",
       "青海        西宁      69.128697\n",
       "黑龙江       哈尔滨     71.075575\n",
       "Name: aqi, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按省市分组，计算平均空气质量指数\n",
    "province_city_aqi = df_all.groupby(['province', 'city']).mean(numeric_only=True)['aqi']\n",
    "\n",
    "province_city_aqi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "977c254d-e214-4036-a2b2-1575fbb97d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>aqi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>河北</td>\n",
       "      <td>石家庄</td>\n",
       "      <td>99.263417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>河南</td>\n",
       "      <td>郑州</td>\n",
       "      <td>97.815991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>陕西</td>\n",
       "      <td>西安</td>\n",
       "      <td>95.228368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>山西</td>\n",
       "      <td>太原</td>\n",
       "      <td>94.813253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>山东</td>\n",
       "      <td>济南</td>\n",
       "      <td>94.667579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>天津</td>\n",
       "      <td>天津</td>\n",
       "      <td>89.334611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>新疆</td>\n",
       "      <td>乌鲁木齐</td>\n",
       "      <td>85.399781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>甘肃</td>\n",
       "      <td>兰州</td>\n",
       "      <td>80.018072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>78.276013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>湖北</td>\n",
       "      <td>武汉</td>\n",
       "      <td>78.246440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>宁夏</td>\n",
       "      <td>银川</td>\n",
       "      <td>77.437808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>四川</td>\n",
       "      <td>成都</td>\n",
       "      <td>76.407996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>内蒙古</td>\n",
       "      <td>呼和浩特</td>\n",
       "      <td>75.790252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>江苏</td>\n",
       "      <td>南京</td>\n",
       "      <td>75.762322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>湖南</td>\n",
       "      <td>长沙</td>\n",
       "      <td>74.406575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>安徽</td>\n",
       "      <td>合肥</td>\n",
       "      <td>74.355969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>辽宁</td>\n",
       "      <td>沈阳</td>\n",
       "      <td>73.671413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>浙江</td>\n",
       "      <td>杭州</td>\n",
       "      <td>71.651150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>黑龙江</td>\n",
       "      <td>哈尔滨</td>\n",
       "      <td>71.075575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>青海</td>\n",
       "      <td>西宁</td>\n",
       "      <td>69.128697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>江西</td>\n",
       "      <td>南昌</td>\n",
       "      <td>67.444688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>67.418949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>吉林</td>\n",
       "      <td>长春</td>\n",
       "      <td>66.472070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>重庆</td>\n",
       "      <td>重庆</td>\n",
       "      <td>66.189485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>广东</td>\n",
       "      <td>广州</td>\n",
       "      <td>65.273823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>西藏</td>\n",
       "      <td>拉萨</td>\n",
       "      <td>52.811062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>广西</td>\n",
       "      <td>南宁</td>\n",
       "      <td>51.984666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>福建</td>\n",
       "      <td>福州</td>\n",
       "      <td>51.550931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>云南</td>\n",
       "      <td>昆明</td>\n",
       "      <td>51.334064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>贵州</td>\n",
       "      <td>贵阳</td>\n",
       "      <td>46.776013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>海南</td>\n",
       "      <td>海口</td>\n",
       "      <td>43.872946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   province  city        aqi\n",
       "16       河北   石家庄  99.263417\n",
       "17       河南    郑州  97.815991\n",
       "28       陕西    西安  95.228368\n",
       "10       山西    太原  94.813253\n",
       "9        山东    济南  94.667579\n",
       "6        天津    天津  89.334611\n",
       "13       新疆  乌鲁木齐  85.399781\n",
       "22       甘肃    兰州  80.018072\n",
       "3        北京    北京  78.276013\n",
       "20       湖北    武汉  78.246440\n",
       "7        宁夏    银川  77.437808\n",
       "5        四川    成都  76.407996\n",
       "2       内蒙古  呼和浩特  75.790252\n",
       "14       江苏    南京  75.762322\n",
       "21       湖南    长沙  74.406575\n",
       "8        安徽    合肥  74.355969\n",
       "26       辽宁    沈阳  73.671413\n",
       "18       浙江    杭州  71.651150\n",
       "30      黑龙江   哈尔滨  71.075575\n",
       "29       青海    西宁  69.128697\n",
       "15       江西    南昌  67.444688\n",
       "0        上海    上海  67.418949\n",
       "4        吉林    长春  66.472070\n",
       "27       重庆    重庆  66.189485\n",
       "11       广东    广州  65.273823\n",
       "24       西藏    拉萨  52.811062\n",
       "12       广西    南宁  51.984666\n",
       "23       福建    福州  51.550931\n",
       "1        云南    昆明  51.334064\n",
       "25       贵州    贵阳  46.776013\n",
       "19       海南    海口  43.872946"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "province_city_aqi_df = province_city_aqi.reset_index()\n",
    "province_city_aqi_df = province_city_aqi_df.sort_values(by='aqi', ascending=False)\n",
    "province_city_aqi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ee448330-f1fb-4e01-b6d1-95b9d668e5a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个透视表，行为省份，列为城市，值为AQI平均值\n",
    "pivot_table = province_city_aqi_df.pivot(index='province', columns='city', values='aqi')\n",
    "\n",
    "# 生成热力图\n",
    "plt.figure(figsize=(15, 13))\n",
    "sns.heatmap(pivot_table, cmap='YlGnBu', linewidths=.5, annot=True, fmt=\".0f\")\n",
    "plt.title(\"不同省份和城市的平均AQI热力图\", fontsize=18)\n",
    "plt.xlabel(\"城市\", fontsize=18)\n",
    "plt.ylabel(\"省份\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96f8458-5dcf-40c2-b3ef-e4dc1446a6b2",
   "metadata": {},
   "source": [
    "如上图所示，我们可以看到不同省份和城市的平均AQI热力图。从图中可以观察到以下几点：\n",
    "\n",
    "- 空气质量差异：不同城市和省份的空气质量有显著差异。一些地区的平均AQI较高，而其他地区的平均AQI较低。\n",
    "- 地理位置影响：地理位置可能对空气质量有重要影响。例如，沿海城市通常空气质量较好，而内陆城市可能由于工业和交通活动而空气质量较差。\n",
    "- 季节性因素：由于数据是按月平均的，季节性因素可能也对空气质量有影响。例如，冬季由于取暖活动，空气质量可能较差。\n",
    "\n",
    "总体来看，空气质量在不同地区之间有显著差异，这可能与地理位置、工业活动、交通排放和季节性因素有关。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "28a9756f-714c-43f1-a7f0-2f53debe8522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 城市AQI排行\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 确保df已经按照AQI值排序\n",
    "province_city_aqi_df_sorted = province_city_aqi_df.sort_values(by='aqi', ascending=False)\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(18, 12))  # 根据需要，您可以调整图片的大小\n",
    "sns.barplot(x='aqi', y='city', data=province_city_aqi_df_sorted)\n",
    "plt.title(\"城市AQI排行\", fontsize=18)\n",
    "plt.xlabel(\"平均AQI\", fontsize=18)\n",
    "plt.ylabel(\"城市\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308011d4-ad0a-4f97-a89d-2837ab7d6642",
   "metadata": {},
   "source": [
    "### ③ 污染物浓度分布"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0870d6b6-224e-4ebb-a690-366237ddbd61",
   "metadata": {},
   "source": [
    "- 浓度分布分析：我们可以为每种污染物创建直方图，用来查看其浓度的分布情况\n",
    "- 时间趋势分布：我们可以创建折线图，以查看每种污染物随时间的浓度变化趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "40dc9bee-dd11-425d-a939-08d5652138b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>time</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>complexindex</th>\n",
       "      <th>rank</th>\n",
       "      <th>primary_pollutant</th>\n",
       "      <th>temp</th>\n",
       "      <th>humi</th>\n",
       "      <th>windlevel</th>\n",
       "      <th>winddirection</th>\n",
       "      <th>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>68</td>\n",
       "      <td>2.69167</td>\n",
       "      <td>66.0</td>\n",
       "      <td>—</td>\n",
       "      <td>3.625</td>\n",
       "      <td>60.292</td>\n",
       "      <td>0.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>87</td>\n",
       "      <td>64</td>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>0.9</td>\n",
       "      <td>68</td>\n",
       "      <td>4.84881</td>\n",
       "      <td>188.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>4.458</td>\n",
       "      <td>61.125</td>\n",
       "      <td>0.250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>58</td>\n",
       "      <td>0.7</td>\n",
       "      <td>55</td>\n",
       "      <td>4.21875</td>\n",
       "      <td>135.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>6.958</td>\n",
       "      <td>66.000</td>\n",
       "      <td>0.292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>75</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>0.6</td>\n",
       "      <td>47</td>\n",
       "      <td>2.87232</td>\n",
       "      <td>181.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>9.048</td>\n",
       "      <td>91.286</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>0.7</td>\n",
       "      <td>42</td>\n",
       "      <td>3.19821</td>\n",
       "      <td>175.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>7.708</td>\n",
       "      <td>96.667</td>\n",
       "      <td>0.083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  province city        time  aqi  pm2_5  pm10  so2  no2   co  o3  \\\n",
       "0       上海   上海  2019-01-01   40     28    35    7   29  0.5  68   \n",
       "1       上海   上海  2019-01-02   87     64    72   13   45  0.9  68   \n",
       "2       上海   上海  2019-01-03   73     47    53    9   58  0.7  55   \n",
       "3       上海   上海  2019-01-04   75     18    22    6   60  0.6  47   \n",
       "4       上海   上海  2019-01-05   64     32    33    6   51  0.7  42   \n",
       "\n",
       "   complexindex   rank primary_pollutant   temp    humi  windlevel  \\\n",
       "0       2.69167   66.0                 —  3.625  60.292      0.500   \n",
       "1       4.84881  188.0        细颗粒物:PM2.5  4.458  61.125      0.250   \n",
       "2       4.21875  135.0              二氧化氮  6.958  66.000      0.292   \n",
       "3       2.87232  181.0              二氧化氮  9.048  91.286      0.000   \n",
       "4       3.19821  175.0              二氧化氮  7.708  96.667      0.083   \n",
       "\n",
       "   winddirection weather  \n",
       "0            NaN     NaN  \n",
       "1            NaN     NaN  \n",
       "2            NaN     NaN  \n",
       "3            NaN     NaN  \n",
       "4            NaN     NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7d1b1ac5-02a1-41f0-a352-a257545194d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x936 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为每种污染物创建一个直方图，以展示在数据集中的分布情况\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建6个子图，为每种污染物绘制直方图\n",
    "fig, axs = plt.subplots(2, 3, figsize=(20, 13))  # 2行3列子图\n",
    "\n",
    "# 直方图列表\n",
    "pollutants = ['pm2_5', 'pm10', 'co', 'no2', 'so2', 'o3']\n",
    "\n",
    "# 在子图上循环绘制直方图\n",
    "for i, pollutant in enumerate(pollutants):\n",
    "    ax = axs[i//3, i%3]  # 计算子图位置\n",
    "    ax.hist(df_all[pollutant].dropna(), bins=30)  # dropna()移除缺失值\n",
    "    ax.set_title(f'污染物{pollutant}的浓度分布', fontsize=18)\n",
    "    ax.set_xlabel(f'{pollutant}污染物浓度', fontsize=18)\n",
    "    ax.set_ylabel('频率', fontsize=18)\n",
    "\n",
    "# 调整子图间距\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79b87f0-3615-4499-b211-fe06d9363f03",
   "metadata": {},
   "source": [
    "从上面直方图中，可知\n",
    "- PM2.5 和 PM10 的浓度分布：这两种颗粒物的浓度分布相对均匀，但也有一些高浓度的峰值。\n",
    "- SO2（二氧化硫）的浓度分布：大多数数据点的浓度较低，但也有一些较高的浓度值。\n",
    "- NO2（二氧化氮）的浓度分布：与SO2类似，大部分数据点的浓度较低，但也有一些较高的浓度值。\n",
    "- CO（一氧化碳）的浓度分布：大多数数据点的浓度集中在较低的水平。\n",
    "- O3（臭氧）的浓度分布：大部分数据点的浓度较低，但也有一些较高的浓度值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98a49d6-b310-4428-aae6-34e0d6abcb4d",
   "metadata": {},
   "source": [
    "### ④ 空气质量等级分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "241a3640-ff2e-4bd7-8621-83144482f834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>time</th>\n",
       "      <th>aqi</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm10</th>\n",
       "      <th>so2</th>\n",
       "      <th>no2</th>\n",
       "      <th>co</th>\n",
       "      <th>o3</th>\n",
       "      <th>complexindex</th>\n",
       "      <th>rank</th>\n",
       "      <th>primary_pollutant</th>\n",
       "      <th>temp</th>\n",
       "      <th>humi</th>\n",
       "      <th>windlevel</th>\n",
       "      <th>winddirection</th>\n",
       "      <th>weather</th>\n",
       "      <th>quality_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>40</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>68</td>\n",
       "      <td>2.69167</td>\n",
       "      <td>66.0</td>\n",
       "      <td>—</td>\n",
       "      <td>3.625</td>\n",
       "      <td>60.292</td>\n",
       "      <td>0.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>优</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>87</td>\n",
       "      <td>64</td>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>0.9</td>\n",
       "      <td>68</td>\n",
       "      <td>4.84881</td>\n",
       "      <td>188.0</td>\n",
       "      <td>细颗粒物:PM2.5</td>\n",
       "      <td>4.458</td>\n",
       "      <td>61.125</td>\n",
       "      <td>0.250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>58</td>\n",
       "      <td>0.7</td>\n",
       "      <td>55</td>\n",
       "      <td>4.21875</td>\n",
       "      <td>135.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>6.958</td>\n",
       "      <td>66.000</td>\n",
       "      <td>0.292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>75</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>0.6</td>\n",
       "      <td>47</td>\n",
       "      <td>2.87232</td>\n",
       "      <td>181.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>9.048</td>\n",
       "      <td>91.286</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>0.7</td>\n",
       "      <td>42</td>\n",
       "      <td>3.19821</td>\n",
       "      <td>175.0</td>\n",
       "      <td>二氧化氮</td>\n",
       "      <td>7.708</td>\n",
       "      <td>96.667</td>\n",
       "      <td>0.083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>良</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  province city        time  aqi  pm2_5  pm10  so2  no2   co  o3  \\\n",
       "0       上海   上海  2019-01-01   40     28    35    7   29  0.5  68   \n",
       "1       上海   上海  2019-01-02   87     64    72   13   45  0.9  68   \n",
       "2       上海   上海  2019-01-03   73     47    53    9   58  0.7  55   \n",
       "3       上海   上海  2019-01-04   75     18    22    6   60  0.6  47   \n",
       "4       上海   上海  2019-01-05   64     32    33    6   51  0.7  42   \n",
       "\n",
       "   complexindex   rank primary_pollutant   temp    humi  windlevel  \\\n",
       "0       2.69167   66.0                 —  3.625  60.292      0.500   \n",
       "1       4.84881  188.0        细颗粒物:PM2.5  4.458  61.125      0.250   \n",
       "2       4.21875  135.0              二氧化氮  6.958  66.000      0.292   \n",
       "3       2.87232  181.0              二氧化氮  9.048  91.286      0.000   \n",
       "4       3.19821  175.0              二氧化氮  7.708  96.667      0.083   \n",
       "\n",
       "   winddirection weather quality_grade  \n",
       "0            NaN     NaN             优  \n",
       "1            NaN     NaN             良  \n",
       "2            NaN     NaN             良  \n",
       "3            NaN     NaN             良  \n",
       "4            NaN     NaN             良  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将AQI的值，按照一定范围划分成“0-50优、51-100良、101-150轻度污染、151-200中度污染、201-300重度污染、>300严重污染”\n",
    "# 定义一个函数，根据AQI值返回质量等级\n",
    "def aqi_to_grade(aqi):\n",
    "    if aqi <= 50:\n",
    "        return '优'\n",
    "    elif aqi <= 100:\n",
    "        return '良'\n",
    "    elif aqi <= 150:\n",
    "        return '轻度污染'\n",
    "    elif aqi <= 200:\n",
    "        return '中度污染'\n",
    "    elif aqi <= 300:\n",
    "        return '重度污染'\n",
    "    else:\n",
    "        return '严重污染'\n",
    "\n",
    "# 应用函数到AQI列，并创建新的列 'quality_grade'\n",
    "df_all['quality_grade'] = df_all['aqi'].apply(aqi_to_grade)\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d6da05e3-0208-4439-83ec-1b128a1b4257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#统计每个QualityGrade的出现次数\n",
    "quality_grade_counts = df_all['quality_grade'].value_counts()\n",
    "\n",
    "#绘制QualityGrade的分布图\n",
    "plt.figure(figsize=(15, 10))\n",
    "quality_grade_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
    "plt.title('空气质量等级分布', fontsize=18)\n",
    "plt.xlabel('空气质量等级', fontsize=18)\n",
    "plt.ylabel('频率', fontsize=18)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3ad29e-dae0-4250-b3f1-a1030d9f92a1",
   "metadata": {},
   "source": [
    "从上面的条形图中，我们可以看到不同空气质量等级的分布情况：\n",
    "- “良”等级的频率最高，意味着在数据集中，空气质量为“良”的天数最多。\n",
    "- “轻度污染”和“优”等级的频数次之。\n",
    "- “中度污染”和“重度污染”的频数相对较低，这表明在数据集中，空气质量达到这两个等级的天数较少。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bff6772-ef0f-4236-ad1f-a29bee18e735",
   "metadata": {},
   "source": [
    "### ⑤ AQI与其他污染物浓度的相关性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26abd687-89c8-4068-bb29-86e0050f42a2",
   "metadata": {},
   "source": [
    "要分析空气质量指数（AQI）与其他污染物浓度之间的相关性，我们可以使用皮尔逊相关系数。皮尔逊相关系数是一个衡量两个变量线性关系强度的统计指标，其值范围从-1到1。接近1或-1的相关系数表示两个变量之间存在强烈的正或负线性关系，而接近0的相关系数表示几乎没有线性关系。\n",
    "我们将计算AQI与以下污染物之间的皮尔逊相关系数：\n",
    "- PM2.5\n",
    "- PM10\n",
    "- SO2\n",
    "- NO2\n",
    "- CO\n",
    "- O3\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56572434-dcde-45dc-97bc-b53e8606e62a",
   "metadata": {},
   "source": [
    "然后，我们可以使用热图来可视化这些相关性。让我们开始计算并可视化这些相关系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2ea4e392-bb4f-44c1-81af-11efc853584e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 提取AQI和6种污染物的列\n",
    "pollutants = ['aqi', 'pm2_5', 'pm10', 'co', 'no2', 'so2', 'o3']\n",
    "subset = df_all[pollutants]\n",
    "\n",
    "# 计算相关矩阵\n",
    "corr_matrix = subset.corr()\n",
    "\n",
    "# 绘制热力图来展示相关性\n",
    "plt.figure(figsize=(15, 13))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title(\"AQI与其他污染物的相关矩阵\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1931cd-2540-4571-a4bf-451869e8a83c",
   "metadata": {},
   "source": [
    "从上面的热图中，我们可以观察到以下信息：\n",
    "\n",
    "- AQI与PM2.5：这两个变量之间的相关性非常高（接近0.9），表明它们之间有强烈的正线性关系。\n",
    "- AQI与PM10：AQI与PM10之间也具有很强的正相关性（约0.85）。\n",
    "- AQI与SO2、NO2和O3：AQI与这些污染物之间也存在正相关性，但相关性相对较低。\n",
    "- AQI与CO：AQI与CO之间的相关性相对较低。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ee27a2-4a7c-4349-b62b-69456b1eea64",
   "metadata": {},
   "source": [
    "这些结果表明，PM2.5和PM10的浓度对空气质量指数（AQI）有显著影响，而其他污染物的影响相对较小。这种分析可以帮助我们更好地理解哪些污染物对空气质量的影响最为重要。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc6b3485-2c57-477f-ac4a-2443d78564db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
